analyzing-malware-sandbox-evasion-techniques
Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports
Best use case
analyzing-malware-sandbox-evasion-techniques is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports
Teams using analyzing-malware-sandbox-evasion-techniques should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-malware-sandbox-evasion-techniques/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-malware-sandbox-evasion-techniques Compares
| Feature / Agent | analyzing-malware-sandbox-evasion-techniques | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Analyzing Malware Sandbox Evasion Techniques ## Overview Sandbox evasion (MITRE ATT&CK T1497) allows malware to detect analysis environments and alter behavior to avoid detection. This skill analyzes behavioral reports from Cuckoo Sandbox and AnyRun for evasion indicators including timing-based checks (GetTickCount, QueryPerformanceCounter, sleep inflation), VM artifact detection (registry keys, MAC address prefixes, process names like vmtoolsd.exe), user interaction checks (mouse movement, keyboard input), and environment fingerprinting (disk size, CPU count, RAM). Detection rules flag samples exhibiting these behaviors for deeper manual analysis. ## When to Use - When investigating security incidents that require analyzing malware sandbox evasion techniques - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Cuckoo Sandbox 2.0+ or AnyRun account for behavioral analysis reports - Python 3.8+ with json library for report parsing - Behavioral report exports in JSON format ## Steps 1. Parse Cuckoo/AnyRun behavioral report JSON files 2. Extract API call sequences for timing-related functions 3. Identify VM artifact detection via registry queries and WMI calls 4. Detect sleep inflation by comparing requested vs actual sleep durations 5. Flag user interaction checks (GetCursorPos, GetAsyncKeyState patterns) 6. Score evasion sophistication based on technique count and diversity 7. Map detected techniques to MITRE ATT&CK T1497 sub-techniques ## Expected Output JSON report listing detected evasion techniques with MITRE ATT&CK mapping, API call evidence, evasion sophistication score, and classification of evasion categories (timing, VM detection, user interaction, environment fingerprinting).
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